Adaptive Sentence Boundary Disambiguation
David D. Palmer and Marti A. Hearst
EECS Department, University of California, Berkeley
Technical Report No. UCB/CSD-94-797
1994
This publication is archived. It is kept only for reference purposes, so it is no longer being updated and may not meet accessibility standards. If you need this content in a different format, please email webteam@eecs.berkeley.edu.
http://www2.eecs.berkeley.edu/Pubs/TechRpts/1994/Archive/CSD-94-797.pdf
Labeling of sentence boundaries is a necessary prerequisite for many natural language processing tasks, including part-of-speech tagging and sentence alignment. End-of-sentence punctuation marks are ambiguous; to disambiguate them most systems use brittle, special-purpose regular expression grammars and exception rules. As an alternative, we have developed an efficient, trainable algorithm that uses a lexicon with part-of-speech probabilities and a feed-forward neural network. After training for less than one minute, the method correctly labels over 98.5% of sentence boundaries in a corpus of over 27,000 sentence-boundary marks. We show the method to be efficient and easily adaptable to different text genres, including single-case texts.
BibTeX citation:
@techreport{Palmer:CSD-94-797,
Author= {Palmer, David D. and Hearst, Marti A.},
Title= {Adaptive Sentence Boundary Disambiguation},
Year= {1994},
Month= {Feb},
Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/1994/6317.html},
Number= {UCB/CSD-94-797},
Abstract= {Labeling of sentence boundaries is a necessary prerequisite for many natural language processing tasks, including part-of-speech tagging and sentence alignment. End-of-sentence punctuation marks are ambiguous; to disambiguate them most systems use brittle, special-purpose regular expression grammars and exception rules. As an alternative, we have developed an efficient, trainable algorithm that uses a lexicon with part-of-speech probabilities and a feed-forward neural network. After training for less than one minute, the method correctly labels over 98.5% of sentence boundaries in a corpus of over 27,000 sentence-boundary marks. We show the method to be efficient and easily adaptable to different text genres, including single-case texts.},
}
EndNote citation:
%0 Report %A Palmer, David D. %A Hearst, Marti A. %T Adaptive Sentence Boundary Disambiguation %I EECS Department, University of California, Berkeley %D 1994 %@ UCB/CSD-94-797 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/1994/6317.html %F Palmer:CSD-94-797